Overview

Brought to you by YData

Dataset statistics

Number of variables25
Number of observations7049
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 MiB
Average record size in memory200.0 B

Variable types

Text6
DateTime3
Categorical9
Numeric7

Alerts

age_category is highly overall correlated with age_of_patient and 4 other fieldsHigh correlation
age_of_patient is highly overall correlated with age_category and 1 other fieldsHigh correlation
base_encounter_cost is highly overall correlated with description and 1 other fieldsHigh correlation
code is highly overall correlated with age_category and 2 other fieldsHigh correlation
description is highly overall correlated with age_category and 3 other fieldsHigh correlation
encounter_class is highly overall correlated with base_encounter_cost and 2 other fieldsHigh correlation
income is highly overall correlated with income_categoryHigh correlation
income_category is highly overall correlated with income and 1 other fieldsHigh correlation
marital is highly overall correlated with age_category and 1 other fieldsHigh correlation
payer_coverage is highly overall correlated with total_claim_costHigh correlation
payer_id is highly overall correlated with age_category and 1 other fieldsHigh correlation
total_claim_cost is highly overall correlated with payer_coverageHigh correlation
ethnicity is highly imbalanced (55.0%) Imbalance
code is highly skewed (γ1 = 59.35486139) Skewed
length_of_stay_hours is highly skewed (γ1 = 31.33577056) Skewed
encounter_id has unique values Unique
payer_coverage has 1930 (27.4%) zeros Zeros

Reproduction

Analysis started2024-12-03 00:13:10.404251
Analysis finished2024-12-03 00:13:19.864798
Duration9.46 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

encounter_id
Text

Unique 

Distinct7049
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size55.2 KiB
2024-12-03T11:13:20.021627image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters253764
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7049 ?
Unique (%)100.0%

Sample

1st row294d0dab-907e-8fce-7a47-0c0d322a5734
2nd row2ccec874-cbaa-e280-7abb-f2bc2b603961
3rd row953c5138-ce17-4084-3432-1ac23f184528
4th row17dd3b88-0b85-2b6f-c342-c9d6cf5315cb
5th row0b03e41b-06a6-66fa-b972-acc5a83b134a
ValueCountFrequency (%)
d1cea2e5-1735-089f-c72f-22ad16976663 1
 
< 0.1%
8fb043ba-08be-2a3a-1011-c3019f2b7d07 1
 
< 0.1%
294d0dab-907e-8fce-7a47-0c0d322a5734 1
 
< 0.1%
2ccec874-cbaa-e280-7abb-f2bc2b603961 1
 
< 0.1%
953c5138-ce17-4084-3432-1ac23f184528 1
 
< 0.1%
17dd3b88-0b85-2b6f-c342-c9d6cf5315cb 1
 
< 0.1%
0b03e41b-06a6-66fa-b972-acc5a83b134a 1
 
< 0.1%
1617912a-d228-1f6c-ed9b-d8fb39ef0a32 1
 
< 0.1%
3606e65a-a4da-810c-ef6b-ddbf6da17952 1
 
< 0.1%
ab6384eb-f097-86af-81c7-dda8af228f77 1
 
< 0.1%
Other values (7039) 7039
99.9%
2024-12-03T11:13:20.336790image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 28196
 
11.1%
c 14291
 
5.6%
b 14275
 
5.6%
7 14271
 
5.6%
0 14244
 
5.6%
d 14158
 
5.6%
2 14114
 
5.6%
a 14101
 
5.6%
4 14086
 
5.6%
f 14069
 
5.5%
Other values (7) 97959
38.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 253764
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 28196
 
11.1%
c 14291
 
5.6%
b 14275
 
5.6%
7 14271
 
5.6%
0 14244
 
5.6%
d 14158
 
5.6%
2 14114
 
5.6%
a 14101
 
5.6%
4 14086
 
5.6%
f 14069
 
5.5%
Other values (7) 97959
38.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 253764
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 28196
 
11.1%
c 14291
 
5.6%
b 14275
 
5.6%
7 14271
 
5.6%
0 14244
 
5.6%
d 14158
 
5.6%
2 14114
 
5.6%
a 14101
 
5.6%
4 14086
 
5.6%
f 14069
 
5.5%
Other values (7) 97959
38.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 253764
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 28196
 
11.1%
c 14291
 
5.6%
b 14275
 
5.6%
7 14271
 
5.6%
0 14244
 
5.6%
d 14158
 
5.6%
2 14114
 
5.6%
a 14101
 
5.6%
4 14086
 
5.6%
f 14069
 
5.5%
Other values (7) 97959
38.6%
Distinct7014
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size55.2 KiB
Minimum1931-04-21 11:59:06
Maximum2024-11-04 08:21:17
2024-12-03T11:13:20.476419image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:13:20.617059image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct7045
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size55.2 KiB
Minimum1931-04-21 12:19:24
Maximum2024-11-05 00:34:10
2024-12-03T11:13:20.745465image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:13:20.883178image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct106
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size55.2 KiB
2024-12-03T11:13:21.108682image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters253764
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30a6452c-4297-a1ac-977a-6a23237c7b46
2nd row30a6452c-4297-a1ac-977a-6a23237c7b46
3rd row30a6452c-4297-a1ac-977a-6a23237c7b46
4th row30a6452c-4297-a1ac-977a-6a23237c7b46
5th row30a6452c-4297-a1ac-977a-6a23237c7b46
ValueCountFrequency (%)
4f159375-4ee4-36ab-b464-6d38f6ff2dae 700
 
9.9%
d1622e8b-d26b-ec81-ffcb-ec4bf2af385b 691
 
9.8%
d27273f0-f62d-7d7f-746d-4565f35cf176 546
 
7.7%
655baba7-47ed-22ac-2093-1196ebb44928 393
 
5.6%
cb1b46a1-9cb5-1187-ccc5-9fb7b98aa957 252
 
3.6%
f3884e8a-8b36-1e93-66dd-e910dfab2ef5 248
 
3.5%
bad5a231-3709-952a-cf44-f8d6a52cc214 243
 
3.4%
14dc5e57-1b84-3305-c042-86c9fc7e4996 141
 
2.0%
17e0bdef-4558-cc1d-2d44-90868cad827b 115
 
1.6%
65016a46-14f4-d19a-f82f-10299aba4c14 99
 
1.4%
Other values (96) 3621
51.4%
2024-12-03T11:13:21.460428image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 28196
 
11.1%
f 17548
 
6.9%
6 16408
 
6.5%
4 15945
 
6.3%
2 15897
 
6.3%
b 15779
 
6.2%
e 14615
 
5.8%
d 14534
 
5.7%
7 14034
 
5.5%
5 13986
 
5.5%
Other values (7) 86822
34.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 253764
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 28196
 
11.1%
f 17548
 
6.9%
6 16408
 
6.5%
4 15945
 
6.3%
2 15897
 
6.3%
b 15779
 
6.2%
e 14615
 
5.8%
d 14534
 
5.7%
7 14034
 
5.5%
5 13986
 
5.5%
Other values (7) 86822
34.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 253764
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 28196
 
11.1%
f 17548
 
6.9%
6 16408
 
6.5%
4 15945
 
6.3%
2 15897
 
6.3%
b 15779
 
6.2%
e 14615
 
5.8%
d 14534
 
5.7%
7 14034
 
5.5%
5 13986
 
5.5%
Other values (7) 86822
34.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 253764
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 28196
 
11.1%
f 17548
 
6.9%
6 16408
 
6.5%
4 15945
 
6.3%
2 15897
 
6.3%
b 15779
 
6.2%
e 14615
 
5.8%
d 14534
 
5.7%
7 14034
 
5.5%
5 13986
 
5.5%
Other values (7) 86822
34.2%
Distinct251
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size55.2 KiB
2024-12-03T11:13:21.687743image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters253764
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)0.5%

Sample

1st rowf2068cee-c75c-321d-9b2c-c33535db89c9
2nd rowf2068cee-c75c-321d-9b2c-c33535db89c9
3rd rowdb106514-f254-3402-b6a4-6d210c78c7e2
4th rowf8918a95-31e8-3ac4-8d12-29ca6080ebda
5th rowf2068cee-c75c-321d-9b2c-c33535db89c9
ValueCountFrequency (%)
6170552c-4d27-3e64-8068-64a380f1837d 717
 
10.2%
0d1570ab-371c-3898-9397-95905d8c5166 646
 
9.2%
ce4d0501-49eb-396a-bc56-545fdfc624b0 566
 
8.0%
38508743-50d1-3429-8fe2-37e42ddaf20e 485
 
6.9%
c272e12f-e154-3fee-b05d-b7c0cab2657d 205
 
2.9%
845fbd9b-2d1c-39a8-8261-28ae40e4fab2 183
 
2.6%
2836d9ac-c6a6-39d1-8f13-fdf0fc8928f4 165
 
2.3%
e59fb2d5-508c-38c6-af2e-21d8216dd2b0 160
 
2.3%
5018c664-e283-30eb-932a-529d9a19b3b5 125
 
1.8%
ea8d287f-b3a9-3800-8798-788e334ee406 107
 
1.5%
Other values (241) 3690
52.3%
2024-12-03T11:13:22.044805image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 28196
 
11.1%
3 18351
 
7.2%
8 17255
 
6.8%
0 16782
 
6.6%
5 15616
 
6.2%
6 14478
 
5.7%
d 14345
 
5.7%
9 14139
 
5.6%
4 13966
 
5.5%
2 13725
 
5.4%
Other values (7) 86911
34.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 253764
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 28196
 
11.1%
3 18351
 
7.2%
8 17255
 
6.8%
0 16782
 
6.6%
5 15616
 
6.2%
6 14478
 
5.7%
d 14345
 
5.7%
9 14139
 
5.6%
4 13966
 
5.5%
2 13725
 
5.4%
Other values (7) 86911
34.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 253764
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 28196
 
11.1%
3 18351
 
7.2%
8 17255
 
6.8%
0 16782
 
6.6%
5 15616
 
6.2%
6 14478
 
5.7%
d 14345
 
5.7%
9 14139
 
5.6%
4 13966
 
5.5%
2 13725
 
5.4%
Other values (7) 86911
34.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 253764
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 28196
 
11.1%
3 18351
 
7.2%
8 17255
 
6.8%
0 16782
 
6.6%
5 15616
 
6.2%
6 14478
 
5.7%
d 14345
 
5.7%
9 14139
 
5.6%
4 13966
 
5.5%
2 13725
 
5.4%
Other values (7) 86911
34.2%
Distinct251
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size55.2 KiB
2024-12-03T11:13:22.257630image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters253764
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)0.5%

Sample

1st rowc3d07214-c20f-3f33-ad41-0e55adf5b024
2nd rowc3d07214-c20f-3f33-ad41-0e55adf5b024
3rd row2c4b7d17-0ded-3e16-b5eb-6dda1d6a81bb
4th rowb4d9fbc9-fdca-369d-bbba-019479923f08
5th rowc3d07214-c20f-3f33-ad41-0e55adf5b024
ValueCountFrequency (%)
5cbe7b88-71ba-3b7c-b027-ab8324c29eef 717
 
10.2%
8ba9dc63-e8c2-383a-9031-314602010985 646
 
9.2%
d946aa55-e4bc-3444-a5e2-1333c809aed2 566
 
8.0%
ab0d6460-0522-3dd7-a8a9-8dffa8b3cd1d 485
 
6.9%
5dfdc01b-32a6-35ad-84b1-9bbfebcd55a4 205
 
2.9%
e25d4fb8-95fd-396b-bd2a-974be06b00fc 183
 
2.6%
3b55f437-786a-34f2-9c07-8dd10fdae467 165
 
2.3%
c9b58d6b-45da-3a51-8644-8fcee6382e1d 160
 
2.3%
3ea4ea33-cbe6-3f5f-a6fb-253ecdd58071 125
 
1.8%
465384bd-0282-3ed1-8329-bc70c094a505 107
 
1.5%
Other values (241) 3690
52.3%
2024-12-03T11:13:22.550561image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 28196
 
11.1%
3 20801
 
8.2%
b 17668
 
7.0%
a 17398
 
6.9%
8 15829
 
6.2%
2 14261
 
5.6%
d 14089
 
5.6%
9 14026
 
5.5%
5 13573
 
5.3%
0 13150
 
5.2%
Other values (7) 84773
33.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 253764
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 28196
 
11.1%
3 20801
 
8.2%
b 17668
 
7.0%
a 17398
 
6.9%
8 15829
 
6.2%
2 14261
 
5.6%
d 14089
 
5.6%
9 14026
 
5.5%
5 13573
 
5.3%
0 13150
 
5.2%
Other values (7) 84773
33.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 253764
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 28196
 
11.1%
3 20801
 
8.2%
b 17668
 
7.0%
a 17398
 
6.9%
8 15829
 
6.2%
2 14261
 
5.6%
d 14089
 
5.6%
9 14026
 
5.5%
5 13573
 
5.3%
0 13150
 
5.2%
Other values (7) 84773
33.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 253764
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 28196
 
11.1%
3 20801
 
8.2%
b 17668
 
7.0%
a 17398
 
6.9%
8 15829
 
6.2%
2 14261
 
5.6%
d 14089
 
5.6%
9 14026
 
5.5%
5 13573
 
5.3%
0 13150
 
5.2%
Other values (7) 84773
33.4%

payer_id
Categorical

High correlation 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size55.2 KiB
a735bf55-83e9-331a-899d-a82a60b9f60c
3155 
e03e23c9-4df1-3eb6-a62d-f70f02301496
759 
df166300-5a78-3502-a46a-832842197811
725 
26aab0cd-6aba-3e1b-ac5b-05c8867e762c
613 
b046940f-1664-3047-bca7-dfa76be352a4
566 
Other values (5)
1231 

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters253764
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowd31fccc3-1767-390d-966a-22a5156f4219
2nd rowd31fccc3-1767-390d-966a-22a5156f4219
3rd rowd31fccc3-1767-390d-966a-22a5156f4219
4th rowd31fccc3-1767-390d-966a-22a5156f4219
5th rowd31fccc3-1767-390d-966a-22a5156f4219

Common Values

ValueCountFrequency (%)
a735bf55-83e9-331a-899d-a82a60b9f60c 3155
44.8%
e03e23c9-4df1-3eb6-a62d-f70f02301496 759
 
10.8%
df166300-5a78-3502-a46a-832842197811 725
 
10.3%
26aab0cd-6aba-3e1b-ac5b-05c8867e762c 613
 
8.7%
b046940f-1664-3047-bca7-dfa76be352a4 566
 
8.0%
d31fccc3-1767-390d-966a-22a5156f4219 424
 
6.0%
0133f751-9229-3cfd-815f-b6d4979bdd6a 346
 
4.9%
8fa6c185-e44e-3e34-8bd8-39be8694f4ce 220
 
3.1%
734afbd6-4794-363b-9bc0-6a3981533ed5 176
 
2.5%
d18ef2e6-ef40-324c-be54-34a5ee865625 65
 
0.9%

Length

2024-12-03T11:13:22.673140image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-03T11:13:22.803280image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
a735bf55-83e9-331a-899d-a82a60b9f60c 3155
44.8%
e03e23c9-4df1-3eb6-a62d-f70f02301496 759
 
10.8%
df166300-5a78-3502-a46a-832842197811 725
 
10.3%
26aab0cd-6aba-3e1b-ac5b-05c8867e762c 613
 
8.7%
b046940f-1664-3047-bca7-dfa76be352a4 566
 
8.0%
d31fccc3-1767-390d-966a-22a5156f4219 424
 
6.0%
0133f751-9229-3cfd-815f-b6d4979bdd6a 346
 
4.9%
8fa6c185-e44e-3e34-8bd8-39be8694f4ce 220
 
3.1%
734afbd6-4794-363b-9bc0-6a3981533ed5 176
 
2.5%
d18ef2e6-ef40-324c-be54-34a5ee865625 65
 
0.9%

Most occurring characters

ValueCountFrequency (%)
- 28196
11.1%
3 23732
 
9.4%
a 22148
 
8.7%
9 19053
 
7.5%
6 19029
 
7.5%
0 15456
 
6.1%
8 15343
 
6.0%
5 15079
 
5.9%
f 13076
 
5.2%
b 12944
 
5.1%
Other values (7) 69708
27.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 253764
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 28196
11.1%
3 23732
 
9.4%
a 22148
 
8.7%
9 19053
 
7.5%
6 19029
 
7.5%
0 15456
 
6.1%
8 15343
 
6.0%
5 15079
 
5.9%
f 13076
 
5.2%
b 12944
 
5.1%
Other values (7) 69708
27.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 253764
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 28196
11.1%
3 23732
 
9.4%
a 22148
 
8.7%
9 19053
 
7.5%
6 19029
 
7.5%
0 15456
 
6.1%
8 15343
 
6.0%
5 15079
 
5.9%
f 13076
 
5.2%
b 12944
 
5.1%
Other values (7) 69708
27.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 253764
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 28196
11.1%
3 23732
 
9.4%
a 22148
 
8.7%
9 19053
 
7.5%
6 19029
 
7.5%
0 15456
 
6.1%
8 15343
 
6.0%
5 15079
 
5.9%
f 13076
 
5.2%
b 12944
 
5.1%
Other values (7) 69708
27.5%

encounter_class
Categorical

High correlation 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size55.2 KiB
ambulatory
4117 
wellness
1281 
outpatient
984 
urgentcare
 
313
emergency
 
246
Other values (5)
 
108

Length

Max length10
Median length10
Mean length9.556391
Min length3

Characters and Unicode

Total characters67363
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwellness
2nd rowwellness
3rd rowemergency
4th rowambulatory
5th rowwellness

Common Values

ValueCountFrequency (%)
ambulatory 4117
58.4%
wellness 1281
 
18.2%
outpatient 984
 
14.0%
urgentcare 313
 
4.4%
emergency 246
 
3.5%
inpatient 56
 
0.8%
home 21
 
0.3%
virtual 12
 
0.2%
snf 11
 
0.2%
hospice 8
 
0.1%

Length

2024-12-03T11:13:22.980802image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-03T11:13:23.107413image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
ambulatory 4117
58.4%
wellness 1281
 
18.2%
outpatient 984
 
14.0%
urgentcare 313
 
4.4%
emergency 246
 
3.5%
inpatient 56
 
0.8%
home 21
 
0.3%
virtual 12
 
0.2%
snf 11
 
0.2%
hospice 8
 
0.1%

Most occurring characters

ValueCountFrequency (%)
a 9599
14.2%
t 7506
11.1%
l 6691
9.9%
u 5426
8.1%
o 5130
7.6%
r 5001
7.4%
e 4995
7.4%
m 4384
6.5%
y 4363
6.5%
b 4117
6.1%
Other values (10) 10151
15.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 67363
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 9599
14.2%
t 7506
11.1%
l 6691
9.9%
u 5426
8.1%
o 5130
7.6%
r 5001
7.4%
e 4995
7.4%
m 4384
6.5%
y 4363
6.5%
b 4117
6.1%
Other values (10) 10151
15.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 67363
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 9599
14.2%
t 7506
11.1%
l 6691
9.9%
u 5426
8.1%
o 5130
7.6%
r 5001
7.4%
e 4995
7.4%
m 4384
6.5%
y 4363
6.5%
b 4117
6.1%
Other values (10) 10151
15.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 67363
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 9599
14.2%
t 7506
11.1%
l 6691
9.9%
u 5426
8.1%
o 5130
7.6%
r 5001
7.4%
e 4995
7.4%
m 4384
6.5%
y 4363
6.5%
b 4117
6.1%
Other values (10) 10151
15.1%

code
Real number (ℝ)

High correlation  Skewed 

Distinct45
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.288151 × 1011
Minimum1505002
Maximum4.53131 × 1014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2024-12-03T11:13:23.259383image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum1505002
5-th percentile1.62673 × 108
Q11.85347 × 108
median1.85347 × 108
Q33.0833501 × 108
95-th percentile6.98314 × 108
Maximum4.53131 × 1014
Range4.53131 × 1014
Interquartile range (IQR)1.2298801 × 108

Descriptive statistics

Standard deviation7.6320985 × 1012
Coefficient of variation (CV)59.248478
Kurtosis3521.9989
Mean1.288151 × 1011
Median Absolute Deviation (MAD)2002
Skewness59.354861
Sum9.0801762 × 1014
Variance5.8248927 × 1025
MonotonicityNot monotonic
2024-12-03T11:13:23.393191image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
185347001 2371
33.6%
185349003 1207
17.1%
162673000 843
 
12.0%
410620009 432
 
6.1%
185345009 361
 
5.1%
424619006 318
 
4.5%
702927004 311
 
4.4%
308335008 226
 
3.2%
33879002 136
 
1.9%
698314001 135
 
1.9%
Other values (35) 709
 
10.1%
ValueCountFrequency (%)
1505002 1
 
< 0.1%
32485007 21
 
0.3%
33879002 136
 
1.9%
50849002 130
 
1.8%
56876005 7
 
0.1%
79094001 2
 
< 0.1%
86013001 2
 
< 0.1%
162673000 843
12.0%
169762003 35
 
0.5%
170837001 2
 
< 0.1%
ValueCountFrequency (%)
4.531310001 × 10142
 
< 0.1%
702927004 311
4.4%
698314001 135
 
1.9%
439740005 43
 
0.6%
439708006 21
 
0.3%
424619006 318
4.5%
424441002 52
 
0.7%
410620009 432
6.1%
410410006 7
 
0.1%
397821002 1
 
< 0.1%

description
Categorical

High correlation 

Distinct45
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size55.2 KiB
Encounter for problem (procedure)
2371 
Encounter for check up (procedure)
1207 
General examination of patient (procedure)
843 
Well child visit (procedure)
432 
Encounter for symptom (procedure)
361 
Other values (40)
1835 

Length

Max length75
Median length70
Mean length34.982693
Min length19

Characters and Unicode

Total characters246593
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.1%

Sample

1st rowGeneral examination of patient (procedure)
2nd rowGeneral examination of patient (procedure)
3rd rowEmergency room admission (procedure)
4th rowEncounter for check up (procedure)
5th rowGeneral examination of patient (procedure)

Common Values

ValueCountFrequency (%)
Encounter for problem (procedure) 2371
33.6%
Encounter for check up (procedure) 1207
17.1%
General examination of patient (procedure) 843
 
12.0%
Well child visit (procedure) 432
 
6.1%
Encounter for symptom (procedure) 361
 
5.1%
Prenatal visit (regime/therapy) 318
 
4.5%
Urgent care clinic (environment) 311
 
4.4%
Patient encounter procedure (procedure) 226
 
3.2%
Administration of vaccine to produce active immunity (procedure) 136
 
1.9%
Consultation for treatment (procedure) 135
 
1.9%
Other values (35) 709
 
10.1%

Length

2024-12-03T11:13:23.627224image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
procedure 6565
21.7%
encounter 4300
14.2%
for 4083
13.5%
problem 2371
 
7.8%
check 1207
 
4.0%
up 1207
 
4.0%
patient 1070
 
3.5%
of 981
 
3.2%
visit 902
 
3.0%
general 843
 
2.8%
Other values (78) 6695
22.2%

Most occurring characters

ValueCountFrequency (%)
e 30045
12.2%
r 27954
11.3%
23175
 
9.4%
o 21745
 
8.8%
n 15908
 
6.5%
c 15548
 
6.3%
u 12810
 
5.2%
p 12372
 
5.0%
t 12290
 
5.0%
i 8574
 
3.5%
Other values (34) 66172
26.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 246593
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 30045
12.2%
r 27954
11.3%
23175
 
9.4%
o 21745
 
8.8%
n 15908
 
6.5%
c 15548
 
6.3%
u 12810
 
5.2%
p 12372
 
5.0%
t 12290
 
5.0%
i 8574
 
3.5%
Other values (34) 66172
26.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 246593
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 30045
12.2%
r 27954
11.3%
23175
 
9.4%
o 21745
 
8.8%
n 15908
 
6.5%
c 15548
 
6.3%
u 12810
 
5.2%
p 12372
 
5.0%
t 12290
 
5.0%
i 8574
 
3.5%
Other values (34) 66172
26.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 246593
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 30045
12.2%
r 27954
11.3%
23175
 
9.4%
o 21745
 
8.8%
n 15908
 
6.5%
c 15548
 
6.3%
u 12810
 
5.2%
p 12372
 
5.0%
t 12290
 
5.0%
i 8574
 
3.5%
Other values (34) 66172
26.8%

base_encounter_cost
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108.44756
Minimum75
Maximum146.18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2024-12-03T11:13:23.733137image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum75
5-th percentile85.55
Q185.55
median85.55
Q3136.8
95-th percentile142.58
Maximum146.18
Range71.18
Interquartile range (IQR)51.25

Descriptive statistics

Standard deviation27.050623
Coefficient of variation (CV)0.24943507
Kurtosis-1.8477948
Mean108.44756
Median Absolute Deviation (MAD)0
Skewness0.35037511
Sum764446.83
Variance731.73623
MonotonicityNot monotonic
2024-12-03T11:13:23.829806image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
85.55 4042
57.3%
142.58 1372
 
19.5%
136.8 1281
 
18.2%
146.18 258
 
3.7%
87.71 44
 
0.6%
128.53 21
 
0.3%
110.92 11
 
0.2%
137.53 8
 
0.1%
75 6
 
0.1%
125 6
 
0.1%
ValueCountFrequency (%)
75 6
 
0.1%
85.55 4042
57.3%
87.71 44
 
0.6%
110.92 11
 
0.2%
125 6
 
0.1%
128.53 21
 
0.3%
136.8 1281
 
18.2%
137.53 8
 
0.1%
142.58 1372
 
19.5%
146.18 258
 
3.7%
ValueCountFrequency (%)
146.18 258
 
3.7%
142.58 1372
 
19.5%
137.53 8
 
0.1%
136.8 1281
 
18.2%
128.53 21
 
0.3%
125 6
 
0.1%
110.92 11
 
0.2%
87.71 44
 
0.6%
85.55 4042
57.3%
75 6
 
0.1%

total_claim_cost
Real number (ℝ)

High correlation 

Distinct2317
Distinct (%)32.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2236.3992
Minimum75
Maximum67764.63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2024-12-03T11:13:23.954489image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum75
5-th percentile85.55
Q1535.87
median879.52
Q31791.03
95-th percentile9605.398
Maximum67764.63
Range67689.63
Interquartile range (IQR)1255.16

Descriptive statistics

Standard deviation4834.4294
Coefficient of variation (CV)2.1617023
Kurtosis56.253927
Mean2236.3992
Median Absolute Deviation (MAD)491.45
Skewness6.5009818
Sum15764378
Variance23371708
MonotonicityNot monotonic
2024-12-03T11:13:24.084906image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
535.87 707
 
10.0%
85.55 375
 
5.3%
234.71 322
 
4.6%
1791.03 232
 
3.3%
3105.35 220
 
3.1%
704.2 176
 
2.5%
3536.75 172
 
2.4%
142.58 141
 
2.0%
516.95 140
 
2.0%
278.58 138
 
2.0%
Other values (2307) 4426
62.8%
ValueCountFrequency (%)
75 1
 
< 0.1%
85.55 375
5.3%
87.71 20
 
0.3%
92.93 18
 
0.3%
120.06 3
 
< 0.1%
128.94 1
 
< 0.1%
136.13 1
 
< 0.1%
136.8 22
 
0.3%
142.58 141
 
2.0%
146.18 79
 
1.1%
ValueCountFrequency (%)
67764.63 1
< 0.1%
66384.16 1
< 0.1%
59855.16 1
< 0.1%
57848.21 1
< 0.1%
56814.68 1
< 0.1%
55941.6 1
< 0.1%
54058.11 1
< 0.1%
53912.98 1
< 0.1%
53540.6 1
< 0.1%
53025.01 1
< 0.1%

payer_coverage
Real number (ℝ)

High correlation  Zeros 

Distinct2405
Distinct (%)34.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1541.4883
Minimum0
Maximum56814.68
Zeros1930
Zeros (%)27.4%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2024-12-03T11:13:24.208977image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median436.6
Q31103.8
95-th percentile7838.588
Maximum56814.68
Range56814.68
Interquartile range (IQR)1103.8

Descriptive statistics

Standard deviation3869.6898
Coefficient of variation (CV)2.5103595
Kurtosis56.916004
Mean1541.4883
Median Absolute Deviation (MAD)436.6
Skewness6.3081185
Sum10865951
Variance14974499
MonotonicityNot monotonic
2024-12-03T11:13:24.335936image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1930
27.4%
428.7 605
 
8.6%
187.76 228
 
3.2%
1432.82 187
 
2.7%
2484.28 83
 
1.2%
68.44 75
 
1.1%
2829.4 61
 
0.9%
535.87 45
 
0.6%
35.55 44
 
0.6%
413.56 37
 
0.5%
Other values (2395) 3754
53.3%
ValueCountFrequency (%)
0 1930
27.4%
0.94 1
 
< 0.1%
2.06 1
 
< 0.1%
2.82 1
 
< 0.1%
3.83 1
 
< 0.1%
3.95 1
 
< 0.1%
6.58 1
 
< 0.1%
11.63 1
 
< 0.1%
12.46 1
 
< 0.1%
15.49 1
 
< 0.1%
ValueCountFrequency (%)
56814.68 1
< 0.1%
55891.6 1
< 0.1%
54211.38 1
< 0.1%
54008.11 1
< 0.1%
53107.32 1
< 0.1%
51188.06 1
< 0.1%
50101.53 1
< 0.1%
43721.15 1
< 0.1%
41113.3 1
< 0.1%
40941.51 1
< 0.1%
Distinct106
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size55.2 KiB
2024-12-03T11:13:24.511422image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length17
Median length15
Mean length9.3620372
Min length7

Characters and Unicode

Total characters65993
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)0.3%

Sample

1st rowUnknown
2nd rowUnknown
3rd row125605004.0
4th row359817006.0
5th rowUnknown
ValueCountFrequency (%)
unknown 2436
34.6%
431857002.0 1162
16.5%
46177005.0 673
 
9.5%
72892002.0 392
 
5.6%
103697008.0 325
 
4.6%
389095005.0 323
 
4.6%
66383009.0 317
 
4.5%
254837009.0 158
 
2.2%
444814009.0 91
 
1.3%
82423001.0 83
 
1.2%
Other values (96) 1089
15.4%
2024-12-03T11:13:24.827085image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 15075
22.8%
n 7308
11.1%
. 4613
 
7.0%
7 4121
 
6.2%
2 3573
 
5.4%
5 3435
 
5.2%
1 3363
 
5.1%
8 3289
 
5.0%
3 3195
 
4.8%
4 3026
 
4.6%
Other values (6) 14995
22.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 65993
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 15075
22.8%
n 7308
11.1%
. 4613
 
7.0%
7 4121
 
6.2%
2 3573
 
5.4%
5 3435
 
5.2%
1 3363
 
5.1%
8 3289
 
5.0%
3 3195
 
4.8%
4 3026
 
4.6%
Other values (6) 14995
22.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 65993
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 15075
22.8%
n 7308
11.1%
. 4613
 
7.0%
7 4121
 
6.2%
2 3573
 
5.4%
5 3435
 
5.2%
1 3363
 
5.1%
8 3289
 
5.0%
3 3195
 
4.8%
4 3026
 
4.6%
Other values (6) 14995
22.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 65993
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 15075
22.8%
n 7308
11.1%
. 4613
 
7.0%
7 4121
 
6.2%
2 3573
 
5.4%
5 3435
 
5.2%
1 3363
 
5.1%
8 3289
 
5.0%
3 3195
 
4.8%
4 3026
 
4.6%
Other values (6) 14995
22.7%
Distinct106
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size55.2 KiB
2024-12-03T11:13:25.058246image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length78
Median length76
Mean length25.039722
Min length7

Characters and Unicode

Total characters176505
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)0.3%

Sample

1st rowUnknown
2nd rowUnknown
3rd rowFracture of bone (disorder)
4th rowClosed fracture of hip (disorder)
5th rowUnknown
ValueCountFrequency (%)
disorder 3290
14.6%
unknown 2436
 
10.8%
disease 1870
 
8.3%
chronic 1267
 
5.6%
kidney 1173
 
5.2%
4 1162
 
5.2%
stage 1162
 
5.2%
care 694
 
3.1%
renal 689
 
3.1%
end-stage 673
 
3.0%
Other values (204) 8112
36.0%
2024-12-03T11:13:25.487259image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 17595
 
10.0%
n 16890
 
9.6%
15486
 
8.8%
r 14680
 
8.3%
i 13629
 
7.7%
d 12080
 
6.8%
s 11167
 
6.3%
o 10516
 
6.0%
a 9903
 
5.6%
t 6612
 
3.7%
Other values (44) 47947
27.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 176505
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 17595
 
10.0%
n 16890
 
9.6%
15486
 
8.8%
r 14680
 
8.3%
i 13629
 
7.7%
d 12080
 
6.8%
s 11167
 
6.3%
o 10516
 
6.0%
a 9903
 
5.6%
t 6612
 
3.7%
Other values (44) 47947
27.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 176505
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 17595
 
10.0%
n 16890
 
9.6%
15486
 
8.8%
r 14680
 
8.3%
i 13629
 
7.7%
d 12080
 
6.8%
s 11167
 
6.3%
o 10516
 
6.0%
a 9903
 
5.6%
t 6612
 
3.7%
Other values (44) 47947
27.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 176505
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 17595
 
10.0%
n 16890
 
9.6%
15486
 
8.8%
r 14680
 
8.3%
i 13629
 
7.7%
d 12080
 
6.8%
s 11167
 
6.3%
o 10516
 
6.0%
a 9903
 
5.6%
t 6612
 
3.7%
Other values (44) 47947
27.2%

length_of_stay_hours
Real number (ℝ)

Skewed 

Distinct2362
Distinct (%)33.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1367359
Minimum0.25
Maximum1464.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2024-12-03T11:13:25.623473image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0.25
5-th percentile0.25
Q10.25
median0.82472222
Q32.7666667
95-th percentile3.9166667
Maximum1464.25
Range1464
Interquartile range (IQR)2.5166667

Descriptive statistics

Standard deviation28.096415
Coefficient of variation (CV)8.9572141
Kurtosis1290.9365
Mean3.1367359
Median Absolute Deviation (MAD)0.57472222
Skewness31.335771
Sum22110.852
Variance789.40856
MonotonicityNot monotonic
2024-12-03T11:13:25.753510image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.25 2075
29.4%
1 253
 
3.6%
24 27
 
0.4%
3.05 26
 
0.4%
2.433333333 23
 
0.3%
3.233333333 22
 
0.3%
3.266666667 22
 
0.3%
2.4 21
 
0.3%
3.7 21
 
0.3%
2.733333333 21
 
0.3%
Other values (2352) 4538
64.4%
ValueCountFrequency (%)
0.25 2075
29.4%
0.2511111111 1
 
< 0.1%
0.2513888889 1
 
< 0.1%
0.2516666667 1
 
< 0.1%
0.2522222222 1
 
< 0.1%
0.2527777778 1
 
< 0.1%
0.2533333333 1
 
< 0.1%
0.2541666667 1
 
< 0.1%
0.2544444444 1
 
< 0.1%
0.2547222222 2
 
< 0.1%
ValueCountFrequency (%)
1464.25 1
 
< 0.1%
816.25 1
 
< 0.1%
720.25 1
 
< 0.1%
624 1
 
< 0.1%
591.0502778 1
 
< 0.1%
528 1
 
< 0.1%
336.25 1
 
< 0.1%
312.7402778 1
 
< 0.1%
312.25 3
< 0.1%
275.5919444 1
 
< 0.1%
Distinct100
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size55.2 KiB
Minimum1914-03-03 00:00:00
Maximum2023-03-01 00:00:00
2024-12-03T11:13:25.876994image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:13:26.007464image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

marital
Categorical

High correlation 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size55.2 KiB
M
3670 
Unknown
1432 
D
1169 
S
602 
W
 
176

Length

Max length7
Median length1
Mean length2.2188963
Min length1

Characters and Unicode

Total characters15641
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
M 3670
52.1%
Unknown 1432
 
20.3%
D 1169
 
16.6%
S 602
 
8.5%
W 176
 
2.5%

Length

2024-12-03T11:13:26.137319image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-03T11:13:26.239680image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
m 3670
52.1%
unknown 1432
 
20.3%
d 1169
 
16.6%
s 602
 
8.5%
w 176
 
2.5%

Most occurring characters

ValueCountFrequency (%)
n 4296
27.5%
M 3670
23.5%
U 1432
 
9.2%
k 1432
 
9.2%
o 1432
 
9.2%
w 1432
 
9.2%
D 1169
 
7.5%
S 602
 
3.8%
W 176
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15641
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 4296
27.5%
M 3670
23.5%
U 1432
 
9.2%
k 1432
 
9.2%
o 1432
 
9.2%
w 1432
 
9.2%
D 1169
 
7.5%
S 602
 
3.8%
W 176
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15641
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 4296
27.5%
M 3670
23.5%
U 1432
 
9.2%
k 1432
 
9.2%
o 1432
 
9.2%
w 1432
 
9.2%
D 1169
 
7.5%
S 602
 
3.8%
W 176
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15641
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 4296
27.5%
M 3670
23.5%
U 1432
 
9.2%
k 1432
 
9.2%
o 1432
 
9.2%
w 1432
 
9.2%
D 1169
 
7.5%
S 602
 
3.8%
W 176
 
1.1%

race
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size55.2 KiB
white
5191 
asian
1098 
other
 
484
black
 
249
native
 
27

Length

Max length6
Median length5
Mean length5.0038303
Min length5

Characters and Unicode

Total characters35272
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwhite
2nd rowwhite
3rd rowwhite
4th rowwhite
5th rowwhite

Common Values

ValueCountFrequency (%)
white 5191
73.6%
asian 1098
 
15.6%
other 484
 
6.9%
black 249
 
3.5%
native 27
 
0.4%

Length

2024-12-03T11:13:26.359046image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-03T11:13:26.471065image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
white 5191
73.6%
asian 1098
 
15.6%
other 484
 
6.9%
black 249
 
3.5%
native 27
 
0.4%

Most occurring characters

ValueCountFrequency (%)
i 6316
17.9%
e 5702
16.2%
t 5702
16.2%
h 5675
16.1%
w 5191
14.7%
a 2472
 
7.0%
n 1125
 
3.2%
s 1098
 
3.1%
o 484
 
1.4%
r 484
 
1.4%
Other values (5) 1023
 
2.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 35272
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 6316
17.9%
e 5702
16.2%
t 5702
16.2%
h 5675
16.1%
w 5191
14.7%
a 2472
 
7.0%
n 1125
 
3.2%
s 1098
 
3.1%
o 484
 
1.4%
r 484
 
1.4%
Other values (5) 1023
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 35272
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 6316
17.9%
e 5702
16.2%
t 5702
16.2%
h 5675
16.1%
w 5191
14.7%
a 2472
 
7.0%
n 1125
 
3.2%
s 1098
 
3.1%
o 484
 
1.4%
r 484
 
1.4%
Other values (5) 1023
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 35272
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 6316
17.9%
e 5702
16.2%
t 5702
16.2%
h 5675
16.1%
w 5191
14.7%
a 2472
 
7.0%
n 1125
 
3.2%
s 1098
 
3.1%
o 484
 
1.4%
r 484
 
1.4%
Other values (5) 1023
 
2.9%

ethnicity
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.2 KiB
nonhispanic
6386 
hispanic
663 

Length

Max length11
Median length11
Mean length10.717832
Min length8

Characters and Unicode

Total characters75550
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownonhispanic
2nd rownonhispanic
3rd rownonhispanic
4th rownonhispanic
5th rownonhispanic

Common Values

ValueCountFrequency (%)
nonhispanic 6386
90.6%
hispanic 663
 
9.4%

Length

2024-12-03T11:13:26.590548image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-03T11:13:26.700861image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
nonhispanic 6386
90.6%
hispanic 663
 
9.4%

Most occurring characters

ValueCountFrequency (%)
n 19821
26.2%
i 14098
18.7%
h 7049
 
9.3%
s 7049
 
9.3%
a 7049
 
9.3%
p 7049
 
9.3%
c 7049
 
9.3%
o 6386
 
8.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 75550
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 19821
26.2%
i 14098
18.7%
h 7049
 
9.3%
s 7049
 
9.3%
a 7049
 
9.3%
p 7049
 
9.3%
c 7049
 
9.3%
o 6386
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 75550
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 19821
26.2%
i 14098
18.7%
h 7049
 
9.3%
s 7049
 
9.3%
a 7049
 
9.3%
p 7049
 
9.3%
c 7049
 
9.3%
o 6386
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 75550
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 19821
26.2%
i 14098
18.7%
h 7049
 
9.3%
s 7049
 
9.3%
a 7049
 
9.3%
p 7049
 
9.3%
c 7049
 
9.3%
o 6386
 
8.5%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.2 KiB
F
3736 
M
3313 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7049
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
F 3736
53.0%
M 3313
47.0%

Length

2024-12-03T11:13:26.824832image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-03T11:13:26.921930image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
f 3736
53.0%
m 3313
47.0%

Most occurring characters

ValueCountFrequency (%)
F 3736
53.0%
M 3313
47.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7049
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 3736
53.0%
M 3313
47.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7049
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 3736
53.0%
M 3313
47.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7049
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 3736
53.0%
M 3313
47.0%

income
Real number (ℝ)

High correlation 

Distinct100
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111813.19
Minimum7361
Maximum816851
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2024-12-03T11:13:27.026637image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum7361
5-th percentile16969
Q161016
median90297
Q3118047
95-th percentile189277
Maximum816851
Range809490
Interquartile range (IQR)57031

Descriptive statistics

Standard deviation125542.18
Coefficient of variation (CV)1.122785
Kurtosis18.539338
Mean111813.19
Median Absolute Deviation (MAD)27965
Skewness4.1059595
Sum7.8817121 × 108
Variance1.5760839 × 1010
MonotonicityNot monotonic
2024-12-03T11:13:27.190307image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
92537 750
 
10.6%
189277 700
 
9.9%
82922 546
 
7.7%
90297 460
 
6.5%
63727 252
 
3.6%
35860 248
 
3.5%
118047 243
 
3.4%
95344 157
 
2.2%
72413 141
 
2.0%
742063 115
 
1.6%
Other values (90) 3437
48.8%
ValueCountFrequency (%)
7361 90
1.3%
7873 17
 
0.2%
8615 56
0.8%
8752 45
0.6%
10135 11
 
0.2%
10682 40
0.6%
12128 44
0.6%
16969 61
0.9%
17382 72
1.0%
18258 31
 
0.4%
ValueCountFrequency (%)
816851 56
 
0.8%
762068 24
 
0.3%
742063 115
 
1.6%
550030 27
 
0.4%
545255 8
 
0.1%
198522 28
 
0.4%
198442 34
 
0.5%
189277 700
9.9%
188023 77
 
1.1%
179090 68
 
1.0%

income_category
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.2 KiB
high-income
3721 
medium-income
1833 
low-income
1495 

Length

Max length13
Median length11
Mean length11.307987
Min length10

Characters and Unicode

Total characters79710
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhigh-income
2nd rowhigh-income
3rd rowhigh-income
4th rowhigh-income
5th rowhigh-income

Common Values

ValueCountFrequency (%)
high-income 3721
52.8%
medium-income 1833
26.0%
low-income 1495
21.2%

Length

2024-12-03T11:13:27.320726image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-03T11:13:27.438994image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
high-income 3721
52.8%
medium-income 1833
26.0%
low-income 1495
21.2%

Most occurring characters

ValueCountFrequency (%)
i 12603
15.8%
m 10715
13.4%
e 8882
11.1%
o 8544
10.7%
h 7442
9.3%
- 7049
8.8%
n 7049
8.8%
c 7049
8.8%
g 3721
 
4.7%
d 1833
 
2.3%
Other values (3) 4823
 
6.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 79710
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 12603
15.8%
m 10715
13.4%
e 8882
11.1%
o 8544
10.7%
h 7442
9.3%
- 7049
8.8%
n 7049
8.8%
c 7049
8.8%
g 3721
 
4.7%
d 1833
 
2.3%
Other values (3) 4823
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 79710
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 12603
15.8%
m 10715
13.4%
e 8882
11.1%
o 8544
10.7%
h 7442
9.3%
- 7049
8.8%
n 7049
8.8%
c 7049
8.8%
g 3721
 
4.7%
d 1833
 
2.3%
Other values (3) 4823
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 79710
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 12603
15.8%
m 10715
13.4%
e 8882
11.1%
o 8544
10.7%
h 7442
9.3%
- 7049
8.8%
n 7049
8.8%
c 7049
8.8%
g 3721
 
4.7%
d 1833
 
2.3%
Other values (3) 4823
 
6.1%

age_of_patient
Real number (ℝ)

High correlation 

Distinct4749
Distinct (%)67.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.005397
Minimum0
Maximum110.04384
Zeros11
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2024-12-03T11:13:27.562155image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.769863
Q125.871233
median49.134247
Q367.775342
95-th percentile79.547397
Maximum110.04384
Range110.04384
Interquartile range (IQR)41.90411

Descriptive statistics

Standard deviation23.919491
Coefficient of variation (CV)0.5088669
Kurtosis-1.026738
Mean47.005397
Median Absolute Deviation (MAD)19.739726
Skewness-0.25318775
Sum331341.05
Variance572.14204
MonotonicityNot monotonic
2024-12-03T11:13:27.809396image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.16164384 73
 
1.0%
19.17808219 28
 
0.4%
22.1890411 27
 
0.4%
12.0630137 23
 
0.3%
9.01369863 22
 
0.3%
11.04657534 22
 
0.3%
17.14520548 21
 
0.3%
13.07945205 21
 
0.3%
10.03013699 20
 
0.3%
25.2 20
 
0.3%
Other values (4739) 6772
96.1%
ValueCountFrequency (%)
0 11
0.2%
0.05479452055 1
 
< 0.1%
0.09589041096 11
0.2%
0.101369863 1
 
< 0.1%
0.1150684932 1
 
< 0.1%
0.2465753425 1
 
< 0.1%
0.2684931507 12
0.2%
0.4410958904 12
0.2%
0.4602739726 1
 
< 0.1%
0.4931506849 1
 
< 0.1%
ValueCountFrequency (%)
110.0438356 1
< 0.1%
110.0054795 1
< 0.1%
108.9890411 1
< 0.1%
108.6876712 1
< 0.1%
108.0109589 1
< 0.1%
107.9726027 1
< 0.1%
107.6931507 2
< 0.1%
107.3780822 1
< 0.1%
107.339726 1
< 0.1%
107.309589 1
< 0.1%

age_category
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.2 KiB
adult
3979 
senior
2025 
children
1045 

Length

Max length8
Median length5
Mean length5.7320187
Min length5

Characters and Unicode

Total characters40405
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowadult
2nd rowadult
3rd rowadult
4th rowadult
5th rowadult

Common Values

ValueCountFrequency (%)
adult 3979
56.4%
senior 2025
28.7%
children 1045
 
14.8%

Length

2024-12-03T11:13:27.929849image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-03T11:13:28.045067image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
adult 3979
56.4%
senior 2025
28.7%
children 1045
 
14.8%

Most occurring characters

ValueCountFrequency (%)
d 5024
12.4%
l 5024
12.4%
a 3979
9.8%
u 3979
9.8%
t 3979
9.8%
e 3070
7.6%
n 3070
7.6%
i 3070
7.6%
r 3070
7.6%
s 2025
5.0%
Other values (3) 4115
10.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 40405
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 5024
12.4%
l 5024
12.4%
a 3979
9.8%
u 3979
9.8%
t 3979
9.8%
e 3070
7.6%
n 3070
7.6%
i 3070
7.6%
r 3070
7.6%
s 2025
5.0%
Other values (3) 4115
10.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 40405
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 5024
12.4%
l 5024
12.4%
a 3979
9.8%
u 3979
9.8%
t 3979
9.8%
e 3070
7.6%
n 3070
7.6%
i 3070
7.6%
r 3070
7.6%
s 2025
5.0%
Other values (3) 4115
10.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 40405
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 5024
12.4%
l 5024
12.4%
a 3979
9.8%
u 3979
9.8%
t 3979
9.8%
e 3070
7.6%
n 3070
7.6%
i 3070
7.6%
r 3070
7.6%
s 2025
5.0%
Other values (3) 4115
10.2%

Interactions

2024-12-03T11:13:18.368562image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:13:12.299232image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:13:13.766079image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:13:14.709291image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:13:15.612391image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:13:16.461793image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:13:17.335490image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:13:18.624161image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:13:12.566012image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:13:14.081218image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:13:14.970545image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:13:15.857946image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:13:16.684135image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:13:17.562100image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:13:18.725235image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:13:12.768259image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:13:14.178301image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:13:15.073269image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:13:15.957045image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:13:16.783827image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:13:17.665771image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:13:18.836482image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:13:12.958722image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:13:14.278748image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:13:15.197558image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:13:16.061758image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:13:16.886281image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:13:17.774533image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:13:18.925613image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:13:13.164065image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:13:14.378225image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:13:15.307975image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:13:16.151830image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:13:16.983305image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:13:17.995641image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:13:19.025924image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:13:13.354165image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:13:14.478766image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:13:15.408143image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:13:16.257285image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:13:17.086134image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:13:18.129673image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:13:19.142494image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:13:13.565711image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:13:14.593905image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:13:15.513512image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:13:16.358710image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:13:17.190356image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-03T11:13:18.255296image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Correlations

2024-12-03T11:13:28.136214image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
age_categoryage_of_patientbase_encounter_costcodedescriptionencounter_classethnicitygenderincomeincome_categorylength_of_stay_hoursmaritalpayer_coveragepayer_idracetotal_claim_cost
age_category1.0000.9090.2810.5750.5750.2420.2130.2710.3280.2000.0180.6200.0920.5040.3170.096
age_of_patient0.9091.000-0.366-0.1640.3200.1520.3310.4890.2200.3400.4100.5400.2460.3140.486-0.063
base_encounter_cost0.281-0.3661.0000.1730.8240.8610.1720.211-0.0910.134-0.4280.1680.0030.1970.1210.169
code0.575-0.1640.1731.0001.0000.8790.2990.3660.0120.262-0.2180.3530.1620.2500.2340.211
description0.5750.3200.8241.0001.0000.8790.2990.3660.2260.2620.4530.3530.3720.2500.2340.444
encounter_class0.2420.1520.8610.8790.8791.0000.1120.1310.1290.1480.4100.1570.0920.1350.1260.103
ethnicity0.2130.3310.1720.2990.2990.1121.0000.1520.2150.0580.0000.1610.1310.3350.1740.117
gender0.2710.4890.2110.3660.3660.1310.1521.0000.4640.2030.0180.3270.1480.3400.4400.148
income0.3280.220-0.0910.0120.2260.1290.2150.4641.0000.7070.1030.2110.0350.4200.283-0.004
income_category0.2000.3400.1340.2620.2620.1480.0580.2030.7071.0000.0000.2170.0480.6060.2540.041
length_of_stay_hours0.0180.410-0.428-0.2180.4530.4100.0000.0180.1030.0001.0000.0000.2660.0000.0000.303
marital0.6200.5400.1680.3530.3530.1570.1610.3270.2110.2170.0001.0000.0470.3100.3120.059
payer_coverage0.0920.2460.0030.1620.3720.0920.1310.1480.0350.0480.2660.0471.0000.0820.0420.616
payer_id0.5040.3140.1970.2500.2500.1350.3350.3400.4200.6060.0000.3100.0821.0000.2470.073
race0.3170.4860.1210.2340.2340.1260.1740.4400.2830.2540.0000.3120.0420.2471.0000.041
total_claim_cost0.096-0.0630.1690.2110.4440.1030.1170.148-0.0040.0410.3030.0590.6160.0730.0411.000

Missing values

2024-12-03T11:13:19.306720image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-03T11:13:19.694772image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

encounter_idstart_timestop_timepatient_idorganization_idprovider_idpayer_idencounter_classcodedescriptionbase_encounter_costtotal_claim_costpayer_coveragereason_codereason_descriptionlength_of_stay_hoursbirthdatemaritalraceethnicitygenderincomeincome_categoryage_of_patientage_category
0294d0dab-907e-8fce-7a47-0c0d322a57342012-04-01 09:04:482012-04-01 10:02:4730a6452c-4297-a1ac-977a-6a23237c7b46f2068cee-c75c-321d-9b2c-c33535db89c9c3d07214-c20f-3f33-ad41-0e55adf5b024d31fccc3-1767-390d-966a-22a5156f4219wellness162673000General examination of patient (procedure)136.801567.0087.20UnknownUnknown0.9663891994-02-06MwhitenonhispanicM100511high-income18.161644adult
12ccec874-cbaa-e280-7abb-f2bc2b6039612013-04-07 09:04:482013-04-07 09:55:4930a6452c-4297-a1ac-977a-6a23237c7b46f2068cee-c75c-321d-9b2c-c33535db89c9c3d07214-c20f-3f33-ad41-0e55adf5b024d31fccc3-1767-390d-966a-22a5156f4219wellness162673000General examination of patient (procedure)136.80704.200.00UnknownUnknown0.8502781994-02-06MwhitenonhispanicM100511high-income19.178082adult
2953c5138-ce17-4084-3432-1ac23f1845282015-09-28 09:04:482015-09-28 11:02:4830a6452c-4297-a1ac-977a-6a23237c7b46db106514-f254-3402-b6a4-6d210c78c7e22c4b7d17-0ded-3e16-b5eb-6dda1d6a81bbd31fccc3-1767-390d-966a-22a5156f4219emergency50849002Emergency room admission (procedure)146.181008.980.00125605004.0Fracture of bone (disorder)1.9666671994-02-06MwhitenonhispanicM100511high-income21.654795adult
317dd3b88-0b85-2b6f-c342-c9d6cf5315cb2015-10-31 11:02:482015-10-31 11:17:4830a6452c-4297-a1ac-977a-6a23237c7b46f8918a95-31e8-3ac4-8d12-29ca6080ebdab4d9fbc9-fdca-369d-bbba-019479923f08d31fccc3-1767-390d-966a-22a5156f4219ambulatory185349003Encounter for check up (procedure)85.5585.553.95359817006.0Closed fracture of hip (disorder)0.2500001994-02-06MwhitenonhispanicM100511high-income21.745205adult
40b03e41b-06a6-66fa-b972-acc5a83b134a2016-04-10 09:04:482016-04-10 10:00:4530a6452c-4297-a1ac-977a-6a23237c7b46f2068cee-c75c-321d-9b2c-c33535db89c9c3d07214-c20f-3f33-ad41-0e55adf5b024d31fccc3-1767-390d-966a-22a5156f4219wellness162673000General examination of patient (procedure)136.802039.18464.94UnknownUnknown0.9325001994-02-06MwhitenonhispanicM100511high-income22.189041adult
51617912a-d228-1f6c-ed9b-d8fb39ef0a322016-04-24 09:04:482016-04-24 11:43:2530a6452c-4297-a1ac-977a-6a23237c7b46f8918a95-31e8-3ac4-8d12-29ca6080ebdab4d9fbc9-fdca-369d-bbba-019479923f08d31fccc3-1767-390d-966a-22a5156f4219ambulatory185349003Encounter for check up (procedure)85.553105.352484.2866383009.0Gingivitis (disorder)2.6436111994-02-06MwhitenonhispanicM100511high-income22.227397adult
63606e65a-a4da-810c-ef6b-ddbf6da179521986-10-07 19:07:211986-10-07 19:38:4434a4dcc4-35fb-6ad5-ab98-be285c586a4f69eae27b-7445-3652-bd34-b535abe0b011d9d8f8b4-ecbb-3cd1-be3e-96eb5b6058fce03e23c9-4df1-3eb6-a62d-f70f02301496wellness162673000General examination of patient (procedure)136.80704.200.00UnknownUnknown0.5230561968-08-06DwhitenonhispanicM49737medium-income18.180822adult
724f50776-1be9-9789-ba60-03447528a47c1990-10-16 19:07:211990-10-16 19:54:5834a4dcc4-35fb-6ad5-ab98-be285c586a4f9ed61c49-1d0c-3774-b528-d75b50938fe2e68afc41-8284-37b0-8584-3b07e32cc5ffe03e23c9-4df1-3eb6-a62d-f70f02301496wellness162673000General examination of patient (procedure)136.801130.480.00UnknownUnknown0.7936111968-08-06DwhitenonhispanicM49737medium-income22.208219adult
8cc632c61-54a0-35f6-be9f-879875d14c4f1996-10-22 19:07:211996-10-22 19:48:1734a4dcc4-35fb-6ad5-ab98-be285c586a4f9ed61c49-1d0c-3774-b528-d75b50938fe2e68afc41-8284-37b0-8584-3b07e32cc5ffe03e23c9-4df1-3eb6-a62d-f70f02301496wellness162673000General examination of patient (procedure)136.801139.500.00UnknownUnknown0.6822221968-08-06DwhitenonhispanicM49737medium-income28.230137adult
9dd83e406-eae2-49dc-734e-06d34594f3df1999-10-26 19:07:211999-10-26 19:49:1334a4dcc4-35fb-6ad5-ab98-be285c586a4f9ed61c49-1d0c-3774-b528-d75b50938fe2e68afc41-8284-37b0-8584-3b07e32cc5ffe03e23c9-4df1-3eb6-a62d-f70f02301496wellness162673000General examination of patient (procedure)136.80996.160.00UnknownUnknown0.6977781968-08-06DwhitenonhispanicM49737medium-income31.241096adult
encounter_idstart_timestop_timepatient_idorganization_idprovider_idpayer_idencounter_classcodedescriptionbase_encounter_costtotal_claim_costpayer_coveragereason_codereason_descriptionlength_of_stay_hoursbirthdatemaritalraceethnicitygenderincomeincome_categoryage_of_patientage_category
70394eab68ea-1c42-edd6-c666-0a4d63cad7262022-08-14 23:42:232022-08-14 23:57:23f339a5f7-0b09-3072-2b01-7c8e8ca2c1fcbe895734-2bc8-387a-8897-7023aee729261753cdbb-bc77-3db4-9bed-1e9de82bf81aa735bf55-83e9-331a-899d-a82a60b9f60chome439708006Home visit (procedure)128.53559.93447.941871000124103.0Transition from acute care to home-health care (finding)0.2500001951-11-22SasiannonhispanicF92537high-income70.775342senior
7040774b8459-cd26-5ac2-bef3-f017d628b4882022-08-15 23:42:232022-08-15 23:57:23f339a5f7-0b09-3072-2b01-7c8e8ca2c1fcbe895734-2bc8-387a-8897-7023aee729261753cdbb-bc77-3db4-9bed-1e9de82bf81aa735bf55-83e9-331a-899d-a82a60b9f60chome439708006Home visit (procedure)128.53991.33793.061871000124103.0Transition from acute care to home-health care (finding)0.2500001951-11-22SasiannonhispanicF92537high-income70.778082senior
70415db88eb9-408d-acaa-779e-e81fd70e81ec2022-09-01 09:25:232022-09-01 10:06:37f339a5f7-0b09-3072-2b01-7c8e8ca2c1fc936ceb96-2b12-371f-aa68-ee02a9c06f4c6521da89-0296-38bd-b158-83ac049f380aa735bf55-83e9-331a-899d-a82a60b9f60cwellness162673000General examination of patient (procedure)136.801291.561033.24UnknownUnknown0.6872221951-11-22SasiannonhispanicF92537high-income70.824658senior
704294cc3e70-aa21-8b87-b0c5-f7d12dafdd7b2022-09-15 09:25:232022-09-15 15:23:15f339a5f7-0b09-3072-2b01-7c8e8ca2c1fc0fedae9f-701f-3317-9b2f-69aea2202cdc7c355076-7f52-398a-be6e-e77e8104c27ca735bf55-83e9-331a-899d-a82a60b9f60cambulatory185349003Encounter for check up (procedure)85.554830.953864.7666383009.0Gingivitis (disorder)5.9644441951-11-22SasiannonhispanicF92537high-income70.863014senior
7043e3dfa401-6793-85f8-e430-bffb4d0aec1c2023-09-07 09:25:232023-09-07 10:25:17f339a5f7-0b09-3072-2b01-7c8e8ca2c1fc936ceb96-2b12-371f-aa68-ee02a9c06f4c6521da89-0296-38bd-b158-83ac049f380aa735bf55-83e9-331a-899d-a82a60b9f60cwellness162673000General examination of patient (procedure)136.80914.78546.52UnknownUnknown0.9983331951-11-22SasiannonhispanicF92537high-income71.841096senior
7044f78b6556-fbfe-8cfe-1eec-48fb7eabf0df2023-09-14 09:25:232023-09-14 10:42:09f339a5f7-0b09-3072-2b01-7c8e8ca2c1fc0fedae9f-701f-3317-9b2f-69aea2202cdc7c355076-7f52-398a-be6e-e77e8104c27ca735bf55-83e9-331a-899d-a82a60b9f60cambulatory185347001Encounter for problem (procedure)85.551379.751103.8037320007.0Loss of teeth (disorder)1.2794441951-11-22SasiannonhispanicF92537high-income71.860274senior
70456024e199-9bdd-7ada-8ffb-a53ab3eb106d2023-09-28 10:42:092023-09-28 11:18:58f339a5f7-0b09-3072-2b01-7c8e8ca2c1fc0fedae9f-701f-3317-9b2f-69aea2202cdc7c355076-7f52-398a-be6e-e77e8104c27ca735bf55-83e9-331a-899d-a82a60b9f60cambulatory390906007Follow-up encounter (procedure)85.55516.95413.5637320007.0Loss of teeth (disorder)0.6136111951-11-22SasiannonhispanicF92537high-income71.898630senior
704603a0d6f1-5131-1f41-35bb-c965b63a3ebd2024-04-14 09:25:232024-04-14 10:25:23f339a5f7-0b09-3072-2b01-7c8e8ca2c1fcfe30f2b4-9bc8-346e-afba-4455d176654524c044b1-9803-3ce7-8bf4-4222e4ea7074a735bf55-83e9-331a-899d-a82a60b9f60cemergency32485007Hospital admission (procedure)146.18146.180.0095417003.0Primary fibromyalgia syndrome (disorder)1.0000001951-11-22SasiannonhispanicF92537high-income72.443836senior
70473f31f856-ac64-df94-45b2-9710c15536f82024-09-12 09:25:232024-09-12 10:13:03f339a5f7-0b09-3072-2b01-7c8e8ca2c1fc936ceb96-2b12-371f-aa68-ee02a9c06f4c6521da89-0296-38bd-b158-83ac049f380aa735bf55-83e9-331a-899d-a82a60b9f60cwellness162673000General examination of patient (procedure)136.801816.641387.55UnknownUnknown0.7944441951-11-22SasiannonhispanicF92537high-income72.857534senior
70488fb043ba-08be-2a3a-1011-c3019f2b7d072024-09-26 09:25:232024-09-26 16:31:42f339a5f7-0b09-3072-2b01-7c8e8ca2c1fc0fedae9f-701f-3317-9b2f-69aea2202cdc7c355076-7f52-398a-be6e-e77e8104c27ca735bf55-83e9-331a-899d-a82a60b9f60cambulatory185349003Encounter for check up (procedure)85.554830.953864.7666383009.0Gingivitis (disorder)7.1052781951-11-22SasiannonhispanicF92537high-income72.895890senior